Availability of large amount of annotated data has fueled unprecedented success of deep learning in various machine learning tasks [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. Though human learners also require various levels of supervision throughout their lifetime, we make use of the bulk of knowledge acquired so far to make intelligent choices, which guides effective learning.
Drawing a parallel to the machine learning problem of image classification, it is natural to expect a model trained on a huge number of labeled classes (e.g., 1000 classes in ImageNet dataset [7]}) to give meaningful representations to identify and differentiate instances of novel categories. This is the basis for the research efforts in Novel Class Discovery (NCD) setting [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}. Given access to labeled training data from a set of classes, an NCD model identifies novel categories from an unlabeled pool containing instances from a disjoint set of classes.
